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This News marketplace report not anymore Promo btc Big movement and why’s pumup BlackRock’s Bitcoin…
- On October 29, Bitcoin ETFs had net inflows of USD 870 million.
- BlackRock garnered $640 million of the net inflows.
- The net inflows into bitcoin ETFs have been skyrocketing as the US elections approach.
- The total value of Bitcoin ETFs is expected to reach a record soon - 1 million BTC.
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The SALT : Revolutionizing Large Language Model (LLM) Training with Small Language Models (SLMs)
- At the core of LLM development lies an extremely demanding pre-training phase.
- SALT addresses these inefficiencies by bringing SLMs into the picture as “intelligent curators” for data selection and guidance, reducing training time and computational load.
- SALT introduces a two-stage training approach where an SLM steps in as a guide during the early stages of LLM training.
- SALT not only reduced the required training time but also outperformed traditional training methods on several benchmarks, achieving superior quality while reducing computational demands.
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10 Stunning Facts About GPT-4 You Need to Know
- GPT-4 is the fourth generation of OpenAI's foundation model, designed to mimic human-like speech and reasoning.
- GPT-4 is a large multimodal language model that can handle text, images, diagrams, and other visual content.
- Some challenges of GPT-4 include limited capacity, accuracy and reliability, and ethical and legal concerns.
- GPT-4 has a significant global impact and applications in industries such as customer service, content creation, and software development.
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Medium
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Understanding the Vanishing Gradient Problem in Deep Learning
- The vanishing gradient problem occurs in deep neural networks when gradients become very small, halting the learning process in certain layers.
- Activation functions like sigmoid or tanh can contribute to the vanishing gradient problem by mapping large input values to small output ranges.
- Strategies to address the vanishing gradient problem include using the Rectified Linear Unit (ReLU) activation function, batch normalization, gradient clipping, and residual networks.
- Innovations in activation functions, layer design, and normalization techniques have allowed for training deeper networks and overcoming the vanishing gradient problem.
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Medium
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Understanding Gradient Descent: Types, Usage, and Practical Examples
- Batch Gradient Descent uses the entire dataset to calculate the gradient and update parameters once per epoch. It is accurate but computationally expensive for large datasets.
- Stochastic Gradient Descent updates parameters for each data point, using only one example at a time. It is faster but may result in fluctuations or jagged progress.
- Mini-batch Gradient Descent divides data into small batches and calculates gradients for each batch, balancing accuracy and efficiency. It is the most popular in deep learning.
- The choice of gradient descent algorithm depends on data size, computational resources, and model requirements. Mini-batch Gradient Descent is often preferred for its efficiency and balanced performance.
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Multimodal Deep Learning for Time Series Forecasting, Classification, and Analysis
- Multimodal deep learning models have been successful in fusing text and imagery data.
- However, there are fewer models that attempt to fuse time series data with text, imagery, and audio.
- The fusion of time series data with other modalities has practical applications in various industries.
- Examples include forecasting river flow using historical data and satellite imagery, predicting patient mortality using vitals, imaging data, and doctor's notes, and forecasting product sales.
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Medium
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Building a General World Model: Enhancing Organizational Understanding
- A General World Model (GWM) builds an internal representation of an organization’s environment to simulate future scenarios and gain a deep understanding.
- Integrating multiple agents from all departments under one cohesive structure is one of the benefits of a GWM.
- A GWM captures nuances and interdependencies between different departments, enabling informed decisions aligned to strategic objectives.
- A GWM is customizable to fit the unique needs of any organization, leveraging insights to achieve goals irrespective of size or industry.
- A GWM synthesizes vast amounts of data from various sources within the organization, unlocking new knowledge and providing accurate predictions.
- A GWM can integrate and coordinate the work of multiple agents such as teams, automated systems, external partners, or individual processes.
- Data integration, scalability, resistance to change, and ethical considerations are some challenges associated with implementing a GWM.
- The rewards of implementing a GWM are greater efficiency, enhanced decision-making, and a more integrated organization, all making it a worthwhile investment.
- As AI systems like GWMs increasingly become integral to business operations, embracing this technology will lead the way to success.
- Organizations should start exploring GWMs not on the question of if, but rather on how soon to do so.
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Medium
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Mastering Deep Learning — An Introduction to Neural Networks, Applications, and History
- Deep learning is a subset of machine learning that learns features through layers in neural networks.
- There are five popular neural network architectures that serve unique purposes.
- Deep learning has roots dating back to the 1960s, with advancements in the 1980s and a significant breakthrough in 2012 with the AlexNet.
- The versatile applications of deep learning include self-driving cars, virtual assistants, and image recognition.
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Pyimagesearch
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NeRFs Explained: Goodbye Photogrammetry?
- Neural Radiance Fields (NeRFs) removes many of the geometric concepts needed in 3D Reconstruction, particularly in Photogrammetry.
- NeRFs estimate the light, density and color of every point in the air using 3 blocks: input, neural net and rendering.
- Block A: In NeRFs, we capture the scene from multiple viewpoints and generate rays for every pixel of each image.
- Block B: A simple multi-layer perceptron Neural Network in NeRFs regresses the color and density of every point of every ray.
- Block C: To accurately render a 3D scene via volumetric rendering, NeRFs first removes points belonging to the 'air', and then for every point of the ray, learns whether it hits an object, how dense it is, and what’s its color.
- Since its first introduction, there have been multiple versions of NeRFs, from Tiny-NeRFs, to KiloNeRFs and others, making it faster and better resolution.
- Neural Sparse Voxel Fields use voxel-ray rendering instead of regular light ray, making NeRFs 10x faster than before.
- KiloNeRF uses thousands of mini-MLPs, rather than calling one MLP a million times making it 2,500x faster than NeRFs while keeping the same resolution.
- NeRFs have a lot of compute, so it's an offline approach, mainly used for photographing an object and spending around 30+ minutes on its reconstruction.
- NeRFs are pretty effective for generating 3D reconstruction using Deep Learning, but the process can be made faster and easier with newer algorithms such as Gaussian Splatting.
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Medium
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Dual Space Training for GANs
- Dual Space Training is a novel approach to enhance optimization and stability in GANs.
- GANs are a type of deep learning algorithm used for unsupervised learning.
- Over 50% of AI researchers used GANs in their projects by 2020.
- Techniques such as the Wasserstein GAN and regularization methods have become more prevalent.
- GANs are being used in real-world applications such as image generation, data augmentation, and anomaly detection.
- One of the major issues with GANs is mode collapse and training instability.
- Ethical concerns arise with GANs in generating realistic but fake data.
- The future of GANs appears promising, with wider adoption in various industries and continued research in optimization techniques.
- GANs have a significant impact on various industries and societal aspects, necessitating careful consideration of their ethical and regulatory implications.
- Dual Space Training represents a significant innovation for efficient and stable GAN models.
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Medium
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Biomimetic Risk Modeling in Financial Services
- Biomimetic risk modeling applies biological principles, such as ant colony optimization for resource management and immune system threat detection, to develop adaptive financial risk models.
- Biomimetic models aimed at providing a new dimension of resilience and flexibility, crucial for modern financial entities facing dynamic risks.
- Ant Colony Optimization (ACO) principles can be applied to optimize asset allocation, transaction processing, and credit distribution.
- Immune-inspired models mimic the immune system’s threat-detection processes and can be used in fraud detection, cybersecurity, and risk management.
- Neural networks inspired by synaptic plasticity are essential in predictive analytics within asset management and banking.
- Biomimetic model implementation in wealth management can increase revenue, reduce cost through AI-driven asset allocation, and avoid risk through real-time monitoring.
- Biomimetic model deployment in asset management can also bring revenue optimization, cost efficiency, and risk mitigation through automated portfolio management and synaptic learning algorithms.
- In insurance, biomimetic models can be utilized for personalized premium calculations, reducing underwriting costs, and real-time insurance fraud detection.
- In banking, implementation of ant colony and immune-inspired models can optimize credit distribution, minimize manual reconciliation costs, and enhance cybersecurity.
- Biomimetic Risk Modeling represents a paradigm shift in financial risk management and requires a careful planning, proper resource allocation, and a commitment to continuous learning and adaptation using AWS tools.
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Medium
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Mapping Hidden Patterns: A Practical Guide to Kohonen Self-Organizing Maps for Data Clustering and…
- Many datasets contain hidden patterns and relationships that are challenging to identify and interpret using traditional techniques.
- This essay demonstrates the practical application of Kohonen Self-Organizing Maps (SOMs) on a synthetic dataset, covering data preprocessing, hyperparameter tuning, and visualization.
- The SOM successfully organized data into well-defined clusters, achieving a high classification accuracy (98.3%) with the K-Nearest Neighbors (KNN) model.
- SOMs are effective for clustering and visualizing complex data, preserving topological relationships, and enabling accurate classification when combined with other models.
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Medium
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Hacking The System Design: How Search Engine Understand and Deliver Results
- Search engines employ complex processes to ensure users can quickly find relevant content, this blog explores the core components of a search engine, how they operate, and what factors influence their performance.
- Crawling is the first step in how search engines gather information about web pages utilizing web crawlers, spiders, or bots.
- After crawling, indexing takes place where the search engine analyzes and organizes the data collected from web pages into a structured database known as the search index.
- Ranking is the process by which search engines determine the order of indexed pages displayed in response to a user’s query.
- Once the ranking is complete, search engines present the results on a Search Engine Results Page (SERP), including both organic results (unpaid) and paid advertisements.
- Several factors determine how well a page ranks in search results, including content relevance, page speed, and user engagement.
- Continuous learning is a key component where search engines constantly learn from user interactions to improve the accuracy and relevance of their results.
- Search engines can be categorized into different types based on their focus such as general search engines, vertical search engines, meta search engines, and local search engines.
- Search engines evaluate how closely a webpage matches a user’s query using several methods such as keyword matching and semantic search.
- Query rewriting enhances search accuracy by transforming user queries into more effective search terms by utilizing synonym expansion, spelling correction, and contextual understanding.
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Medium
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How LinkedIn Uses AI to Detect Deepfakes in Profile Pictures
- LinkedIn has developed an AI-powered system to detect deepfake profile pictures.
- They trained a deep learning model, EfficientNet-B1, on a diverse dataset of real profile pictures and AI-generated images.
- The model was able to accurately distinguish between real and AI-generated images, achieving a true positive rate of 98% with minimal false positives.
- This solution demonstrates the potential of deep learning in ensuring user authenticity on social media platforms.
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Medium
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Shallow Learning vs. Deep Learning: Is Bigger Always Better?
- In machine learning, the choice of model complexity can make a significant impact on the effectiveness and efficiency of solving different problems.
- Shallow learning algorithms are simpler and often faster, while deep learning models comprise multiple layers and can learn more complex patterns.
- Shallow learning is suitable for simple tasks like credit scoring, while deep learning is beneficial for complex problems like object detection in autonomous vehicles.
- Starting with a shallow model as a baseline is often a good approach, but deep learning excels in tackling sophisticated problems that require larger-scale learning.
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